

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase   :	Optional[Any]												=						logging.get_logger(__name__)

_lowercase   :	Dict												=						{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: List[Any]					=					"roberta-prelayernorm"



	def __init__(  self     :       int			,    _lowercase     :       str=5_02_65			,    _lowercase     :       Optional[Any]=7_68			,    _lowercase     :       List[Any]=12			,    _lowercase     :       Dict=12			,    _lowercase     :       int=30_72			,    _lowercase     :       Dict="gelu"			,    _lowercase     :       str=0.1			,    _lowercase     :       Dict=0.1			,    _lowercase     :       Optional[int]=5_12			,    _lowercase     :       int=2			,    _lowercase     :       Tuple=0.02			,    _lowercase     :       Tuple=1E-12			,    _lowercase     :       Optional[int]=1			,    _lowercase     :       List[Any]=0			,    _lowercase     :       Dict=2			,    _lowercase     :       Dict="absolute"			,    _lowercase     :       Any=True			,    _lowercase     :       str=None			,    **_lowercase     :       List[str]			,    ):
		super().__init__(pad_token_id=_lowercase			,    bos_token_id=_lowercase			,    eos_token_id=_lowercase			,    **_lowercase						)

		__UpperCAmelCase      		= vocab_size
		__UpperCAmelCase      		= hidden_size
		__UpperCAmelCase      		= num_hidden_layers
		__UpperCAmelCase      		= num_attention_heads
		__UpperCAmelCase      		= hidden_act
		__UpperCAmelCase      		= intermediate_size
		__UpperCAmelCase      		= hidden_dropout_prob
		__UpperCAmelCase      		= attention_probs_dropout_prob
		__UpperCAmelCase      		= max_position_embeddings
		__UpperCAmelCase      		= type_vocab_size
		__UpperCAmelCase      		= initializer_range
		__UpperCAmelCase      		= layer_norm_eps
		__UpperCAmelCase      		= position_embedding_type
		__UpperCAmelCase      		= use_cache
		__UpperCAmelCase      		= classifier_dropout






class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	@property
	def        a  (  self     :       Union[str, Any]						):
		if self.task == "multiple-choice":
			__UpperCAmelCase      		= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
		else:
			__UpperCAmelCase      		= {0: '''batch''', 1: '''sequence'''}
		return OrderedDict(
		    [
		        ('''input_ids''', dynamic_axis),
		        ('''attention_mask''', dynamic_axis),
		    ]						)

"""simple docstring"""


from .imports import is_rich_available


if is_rich_available():
	from rich.traceback import install

	install(show_locals=False)

else:
	raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')


